Investment Rating - The report emphasizes a quantitative investment approach that focuses on predicting stock profit growth rankings rather than specific numerical values, aiming for investment returns [1]. Core Insights - The idealized testing indicates that accurately predicting changes in ROE and holding stocks ranked highly can yield excess returns, validating the feasibility of this method [5]. - The introduction of the acceleration concept, which refers to changes in growth rates, can optimize models, enhance prediction accuracy, and reduce risks [1][7]. - The secondary trend extrapolation model, which considers profit growth and acceleration, outperforms linear extrapolation and analyst consensus in terms of prediction success rate (72%) and false positive rate (13%) [8]. - The "Growth Trend Resonance Stock Selection Strategy," which combines the optimized profit prediction model, analyst expectations, valuation, and cash flow factors, has shown excellent performance since 2009, consistently achieving excess returns [9]. - Incorporating machine learning methods, particularly tree models like XGBoost and LightGBM, significantly improves prediction accuracy, achieving a success rate of 85% and reducing the false positive rate to 4.7% [10][18]. Summary by Sections Traditional Economic Investment Approach - Traditional economic investment relies heavily on fundamental research, focusing on deep analysis of individual stocks to understand their business models and future profitability trends [2]. Quantitative Perspective on Economic Investment - The quantitative approach emphasizes breadth over depth, predicting relative rankings of stocks rather than specific profit growth amounts [3]. Validating Quantitative Investment Strategies - Idealized testing can validate the effectiveness of quantitative investment strategies by demonstrating that accurately predicting future ROE changes leads to superior net value performance [5]. Optimizing Profit Prediction Models - The introduction of acceleration in profit prediction models enhances accuracy and reduces risks associated with performance changes [8]. Application of Machine Learning in Profit Prediction - Machine learning models, particularly tree models, are preferred for their ability to handle multiple dimensions of data and capture non-linear relationships, leading to improved prediction accuracy [12][18]. Stock Selection Strategy - The strategy based on the difference Boots prediction factor has shown superior performance across various indices, indicating its effectiveness in stock selection [19][20].

Group 1 - Business analysis (BA) and business intelligence (BI) serve different purposes within organizations, with BA focusing on predicting future trends and BI concentrating on analyzing historical data [1][2] - BA aims to identify growth opportunities, optimize business processes, and enhance efficiency through predictive analytics [7][9][10] - BI's primary goals include historical data analysis, reporting, and data visualization to provide insights into past performance [12][14][15] Group 2 - The key components of BA include predictive models, machine learning algorithms, and data mining techniques, which help in forecasting future trends [20][23][45] - BI relies on dashboards, scorecards, and data warehouses to present historical performance data and facilitate decision-making [46][48][52] Group 3 - Both BA and BI utilize similar tools for data collection, integration, and preparation, such as SQL, Python, and R, but differ in their analytical approaches [55][58] - BA employs statistical analysis and predictive modeling tools, while BI focuses on reporting and visualization tools [55][62] Group 4 - BA and BI complement each other by improving data usage and decision-making within organizations, with BI providing a foundation for BA to build upon [63][65] - The integration of BA and BI enhances operational efficiency and strategic planning, allowing organizations to be proactive rather than reactive [66][67] Group 5 - The main distinction between BA and BI lies in their perspective: BA is future-oriented and predictive, while BI is past-oriented and descriptive [68]

Core Viewpoint - The recent study published by Jonas Frisen's team provides compelling evidence that neurogenesis continues in the adult human hippocampus, addressing a long-standing debate in neuroscience regarding the adaptability of the human brain [2][9]. Group 1: Research Findings - The study identifies proliferating neural progenitors in the adult human hippocampus, confirming that new neurons are generated even in late adulthood [2][6]. - The research utilized advanced techniques such as RNAscope and Xenium to locate these cells, confirming their presence in the dentate gyrus, a region critical for memory formation and cognitive flexibility [7][10]. - The findings indicate that human adult neural progenitor cells share similarities with those in mice, pigs, and monkeys, although there are differences in gene activity among individuals [8]. Group 2: Methodology - The research analyzed brain samples from individuals aged 0 to 78 years, revealing all stages of neural progenitor cells in early childhood and identifying proliferating progenitor cells in adults using the Ki67 antibody and machine learning algorithms [10]. - The study's methodology highlights the importance of single-cell transcriptomics in understanding the neurogenic environment in the adult human brain [11].

Core Viewpoint - The introduction of new pear varieties, particularly "Ningli Early Dew," showcases advancements in breeding techniques aimed at enhancing taste, appearance, and cultivation efficiency in the pear industry [1][2]. Group 1: New Pear Varieties - "Ningli Early Dew" is a new pear variety that matures in late June, which is half a month earlier than traditional early-ripening pears, with a growth period of approximately 90 days from flowering to maturity [1][2]. - The variety has a fruit weight of 280-320 grams and features a small core, providing a sweet and juicy taste experience [1]. - Other new varieties presented include "Ning Early Gold," "Ning Late Green," and a red-skinned pear series, all developed by the Nanjing Agricultural University pear innovation team [1][2]. Group 2: Breeding Techniques - The breeding process for new pear varieties traditionally takes 12 to 15 years; however, the research team has implemented image recognition and machine learning technologies to accelerate this process [2]. - The development of the "Cloud Shang Hou Ji" breeding information platform has standardized data collection and improved the efficiency of new pear variety creation [2]. - The combination of hybrid breeding, bud mutation, and molecular marker selection has significantly enhanced the speed and effectiveness of new variety development [2]. Group 3: Market Impact - The newly introduced varieties cover a range of maturity periods from extremely early to mid-late, ensuring a continuous supply of fresh pears in Jiangsu from late June to early September [4].

Core Viewpoint - The article discusses the revolutionary layered resource scheduling solution proposed by Microcloud Hologram (NASDAQ: HOLO) for blockchain-based Internet of Vehicles (IoV) systems, addressing the limitations of traditional centralized cloud architectures in real-time data exchange and identity management [1][2][6]. Group 1: Challenges of Traditional IoV Systems - Traditional IoV systems rely on centralized cloud architectures, which limit scalability and increase the risk of single points of failure [2][3]. - Dependence on Trusted Third Parties (TTP) undermines system autonomy, affecting data security and privacy [2][3]. Group 2: Introduction of Blockchain Technology - Blockchain technology offers a decentralized, tamper-proof, and highly transparent solution for IoV systems [2]. - The implementation of blockchain in IoV faces challenges such as dynamic network topologies and limited resources [2][3]. Group 3: Layered Resource Scheduling Solution - The layered resource scheduling solution categorizes system resources into multiple levels, allowing for dynamic scheduling and management based on varying needs [3][5]. - This approach enhances data security and reliability while facilitating efficient data exchange and identity management [3][5]. Group 4: Advanced Techniques and Innovations - Microcloud Hologram developed a machine learning-based resource assessment method to accurately predict system resource needs in real-time [3][5]. - The solution incorporates key blockchain technologies such as smart contracts and consensus mechanisms to achieve decentralized identity management and data exchange [6]. Group 5: Impact on the Industry - The successful development and application of the layered resource scheduling solution set a new benchmark for the industry, paving the way for advancements in IoV systems [6].

7月2日，第45届国际预测大会（ISF 2025）在北京圆满闭幕。 国际预测大会是该领域最具权威性的国际学术会议。自1981年创办以来，今年首次在中国大陆举办，吸 引了来自全球35个国家和地区的580位顶尖预测科学学者、行业领袖及政策制定者注册参会，规模创历 史新高，充分展现了预测科学在全球的重要性日益增长及中国在该领域的影响力日益提升。 大会围绕"预测科学的前沿与创新"主题，聚焦人工智能、大数据、经济管理、能源环境、气候变化等关 键领域，设置了13场主旨报告、5场深度工作坊、12个平行论坛共计106个专题分论坛，累计开展348场 学术报告。专家学者们就贝叶斯预测、机器学习、大语言模型、预测不确定性、预测组合等热点议题， 以及预测在宏观经济、金融、供应链、能源、医疗、灾害防控等领域的应用展开了广泛而深入的交流。 据了解，下一届国际预测大会（ISF 2026）将于明年在加拿大举行。 ISF 2025大会报告人。主办方供图 本届大会不仅促进了全球预测科学前沿成果的分享与碰撞，也为深化该领域的国际科研合作与交流搭建 了重要平台，对推动预测科学的发展及其在应对全球挑战中的应用具有重要意义。 国际预测者协会主席Laur ...

Group 1 - The core idea of the article is the transition from tangible to intangible assets, emphasizing that the most valuable companies today rely on data, digital ecosystems, and algorithms rather than physical assets [2][7][30] - The shift from heavy assets to insights has disrupted traditional economic value rules, allowing companies like SaaS startups to revolutionize industries without owning physical infrastructure [2][29] - Digital twins are increasingly mapping the physical world, enabling real-time data collection and optimization, which redefines capabilities in the digital age [6][22] Group 2 - The importance of this transition lies in the fact that intangible assets are now more valuable than tangible ones, shifting board discussions from physical assets to API architecture scalability [7][30] - Digitalization is not merely about faster decision-making but enhancing overall enterprise cognition through embedded intelligence in every value chain segment [16][19] - The four powerful forces shaping modern business due to digitalization are novelty, volatility, disruption, and scope, which require organizations to adapt rapidly to changes [22][23][28] Group 3 - Digitalization creates feedback loops where every action generates signals that influence product roadmaps and operational responses [17][19] - The governance of enterprises in a dynamic world must focus on adaptive architectures and capability-centered planning rather than strict control [30][38] - The rise of digital neural systems allows organizations to connect intentions with impacts, enhancing real-time visibility and decision-making processes [42]

编辑丨王多鱼 排版丨水成文 热纳米光子学 在从能源技术到信息处理等各类技术应用中实现了根本性的突破。从热辐射源到热光伏和热 伪装，精确的光谱工程一直受困于反复试验的方法。与此同时， 机器学习 （ Machine Learning ） 在纳 米光子学和超材料的设计方面展现出了强大的能力。 然而，开发一种通用的设计方法来定制具有超宽带控制和精确带选择性的高性能纳米光子辐射源仍是一项 重大挑战，因为它们受到预定义的几何形状和材料、局部优化陷阱以及传统算法的限制。 2025 年 7 月 2 日，上海交通大学 周涵 教授 、 张荻 教授 、新加坡国立大学 仇成伟 教授 、德克萨斯大 学奥斯汀分校 郑跃兵 教授作为共同通讯作者 （上海交通大学 Chengyu Xiao 为第一作者 ） 在 Nature 期 刊发表了题为： Ultrabroadband and band-selective thermal meta-emitters by machine learning 的研 究论文。 该研究提出了一种基于 机器学习 （ Machine Learning ） 的 通用框架，设计出了 多种超宽带和带选择性 的热元辐射源 （ ...

Core Viewpoint - The article emphasizes the importance of scientific thinking and critical analysis in quantitative investment, highlighting the influence of philosopher Karl Popper on investment strategies and the development of models that seek to identify and exploit market inefficiencies. Group 1: Investment Philosophy - The essence of quantitative investment lies in modeling the securities market using scientific methods to identify reproducible patterns that can influence market behavior [16][6] - The investment approach is heavily influenced by Popper's philosophy of "conjecture and refutation," which encourages the search for rules in an uncertain world [7][56] - The focus on objective analysis helps avoid the pitfalls of linear thinking and cognitive biases that can obscure judgment [2][61] Group 2: Performance Metrics - The performance of the multi-strategy fund, specifically the Nuon Multi-Strategy Mixed Fund, achieved a return of 100.74% over the past year, while the Nuon CSI 300 Index Enhanced Fund outperformed the CSI 300 Index by 2.06% with a return of 15.42% [3][29] - The significant outperformance of the Nuon Multi-Strategy Fund compared to small-cap indices like the CSI 2000 indicates that the excess returns are not merely a result of small-cap exposure but rather from sophisticated modeling techniques [3][34] Group 3: Investment Strategies - The concept of "attention value" in the A-share market suggests that investors frequently shift their focus due to the inability of many companies to meet return expectations, which can be strategically exploited for excess returns in micro-cap stocks [26][4] - The investment strategy emphasizes the importance of understanding the underlying statistical patterns and market behaviors rather than relying solely on historical performance [20][22] Group 4: Machine Learning and Model Development - The transition from multi-factor strategies to machine learning models allows for the capture of non-linear patterns, leading to superior returns that exceed human cognitive limitations [3][30] - The use of machine learning in investment models is seen as a way to enhance predictive capabilities and adapt to rapidly changing market conditions [30][40] Group 5: Market Dynamics and Future Outlook - The article argues that the excess returns from micro-cap stocks in the Chinese market are unlikely to converge due to the unique market dynamics and investor behavior [34][35] - The focus on scientific and systematic approaches in investment is expected to reveal opportunities that are not crowded, as many competitors rely on outdated inductive reasoning [45][46]

Quantitative Models and Construction Methods 1. Model Name: Optimization Replication - **Model Construction Idea**: Simplify the replication of index returns into an optimization model that minimizes tracking error[31][32] - **Model Construction Process**: 1. Define the return sequence of the portfolio as: $ \tilde{R}_{t} = \Sigma_{i=1}^{M} \widetilde{W}_{i,t} \cdot Y_{i,t} = Y_{t} \cdot \overline{W}_{t} $ where $ \widetilde{W}_{i,t} $ represents the weight of asset $i$ at time $t$, and $Y_{i,t}$ is the return of asset $i$ at time $t$[31] 2. Minimize the tracking error (TE): $ w = arg\,min\;TE $ $ TE = \frac{1}{T} \Sigma_{t=1}^{T} (\tilde{R}_{t} - R_{t})^2 $ where $R_{t}$ is the benchmark return at time $t$[32] 3. Add constraints: - Full investment: $ \Sigma_{i=1}^{N} w_{i} = 1 $ - Non-negativity: $ 0 \leq w_{i} \leq 1 $[33][35] 4. Incorporate style and industry neutrality constraints to reduce overfitting: $ z_{low} \leq \frac{X_{s}^{T}w - X_{s}^{T}\tilde{w}}{s_{b}} \leq z_{up} $ $ w_{low}^{I} \leq X_{I}^{T}w - X_{I}^{T}\bar{w} \leq w_{up}^{I} $[36] 5. Solve the optimization problem to determine the weights of individual stocks[37] 2. Model Name: Pair Trading - **Model Construction Idea**: Identify pairs of stocks or sectors with similar trends and exploit mean-reversion characteristics to generate excess returns[57] - **Model Construction Process**: 1. Identify pairs of stocks or sectors with stable relationships based on quantitative or fundamental analysis 2. Overweight weaker-performing assets and underweight stronger-performing ones 3. Capture excess returns when the price spread reverts to its mean, particularly during events like macroeconomic data releases or seasonal effects[57] --- Model Backtesting Results 1. Optimization Replication - **Annualized Return**: 5.76% - **Excess Return**: 3.74% - **Sharpe Ratio**: 0.41 - **Excess Drawdown**: 3.86% - **Excess Win Rate**: 72% - **Information Ratio (IR)**: 1.51 - **Tracking Error**: 2.22%[23][27] --- Quantitative Factors and Construction Methods 1. Factor Name: Quantitative Multi-Factor - **Factor Construction Idea**: Select long-term effective alpha factors and construct a multi-factor portfolio to achieve stable excess returns[46] - **Factor Construction Process**: 1. Define the multi-factor model: $ \begin{bmatrix} r_{1} \\ r_{2} \\ \vdots \\ r_{n} \end{bmatrix} = \begin{bmatrix} x_{11} \\ x_{21} \\ \vdots \\ x_{n1} \end{bmatrix} f_{1} + \begin{bmatrix} x_{12} \\ x_{22} \\ \vdots \\ x_{n2} \end{bmatrix} f_{2} + \cdots + \begin{bmatrix} x_{1m} \\ x_{2m} \\ \vdots \\ x_{nm} \end{bmatrix} f_{m} + \begin{bmatrix} u_{1} \\ u_{2} \\ \vdots \\ u_{n} \end{bmatrix} $ where $r_{i}$ is the excess return of stock $i$, $x_{ij}$ is the exposure of stock $i$ to factor $j$, and $f_{j}$ is the return of factor $j$[46] 2. Select effective single factors, such as volatility, short-selling intention, and liquidity, based on theoretical direction and empirical validation[48] 3. Construct a multi-factor portfolio by combining these factors and optimizing weights[47] 2. Factor Name: Negative Enhancement - **Factor Construction Idea**: Underweight stocks expected to underperform or incur losses, leveraging negative factors or events to generate stable excess returns[56] - **Factor Construction Process**: 1. Identify stocks with negative attributes, such as analyst downgrades, equity pledges, or poor earnings reports 2. Underweight these stocks in the portfolio to reduce potential losses and achieve alpha[56] 3. Factor Name: Machine Learning-Based Alpha - **Factor Construction Idea**: Use deep learning models like TCN (Temporal Convolutional Networks) to extract complex and effective alpha factors from price and volume data[52] - **Factor Construction Process**: 1. Train neural networks on historical price and volume data to identify patterns and relationships 2. Generate alpha factors that outperform traditional genetic programming methods in terms of depth and complexity[51][52] --- Factor Backtesting Results 1. Quantitative Multi-Factor - **Excess Return**: Stable across long-term horizons - **Key Factors**: Volatility, short-selling intention, liquidity, and local pricing[48] 2. Negative Enhancement - **Excess Return**: Achieved through underweighting stocks with negative attributes or events[56] 3. Machine Learning-Based Alpha - **Performance**: Demonstrated superior results compared to traditional factor generation methods[52] --- Index Enhancement Methods 1. IPO Enhancement - **Annualized Return**: 2.13% in 2025 - **Segment Performance**: Sci-Tech Innovation Board (4.34%), ChiNext (2.52%)[67][68] 2. Stock Index Futures - **Annualized Basis Spread**: - CSI 300: -6.75% - SSE 50: -2.48% - CSI 500: -13.60% - CSI 1000: -18.09%[72][73] 3. Block Trades - **Median Discount Rate**: 5.38% (2017-2025), 8.23% in 2025[74][75] 4. Private Placements - **Median Discount Rate**: 14.55% (2017-2025), 11.87% in 2025[77]